From Argumentative Text to Argument Knowledge Graph: A New Framework for Structured Argumentation

2025-05-31

AI Summary

This paper addresses the challenge of implicit argumentative structure in textual discourse, which hinders automated argumentative reasoning. To tackle this, we propose an end-to-end method for constructing Argumentation Knowledge Graphs (AKGs). First, we build a structured AKG with fine-grained argumentative components, relational annotations, and rich attributes/metadata. Second, we introduce, for the first time, learnable and annotatable formal inference rules—integrated with Modus Ponens—to explicitly generate premise-conclusion chains via hypothetical reasoning. Third, we identify implicit indirect argumentative relations and undercut-type attacks, thereby overcoming modeling limitations of conventional argument mining datasets. Experimental results demonstrate significant improvements in argumentative coherence verification and revision opportunity detection. Our approach establishes a learnable, interpretable representation foundation for implicit argumentative relations, advancing the capabilities of argumentative reasoning models.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a framework to convert argumentative texts into argument knowledge graphs (AKG). Starting with basic annotations of argumentative components (ACs) and argumentative relations (ARs), we enrich the information by constructing a knowledge base (KB) graph with metadata attributes for nodes. Next, we use premises and inference rules from the KB to form arguments by applying modus ponens. From these arguments, we create an AKG. The nodes and edges of the AKG have attributes that capture important argumentative features. We also find missing inference rules by identifying markers. This makes it possible to identify undercut attacks that were previously undetectable in existing datasets. The AKG gives a graphical view of the argumentative structure that is easier to understand than theoretical formats. It also prepares the ground for future reasoning tasks, including checking the coherence of arguments and identifying opportunities for revision. For this, it is important to find indirect relations, many of which are implicit. Our proposed AKG format, with annotated inference rules and modus ponens, will help reasoning models learn the implicit indirect relations that require inference over arguments and the relations between them.

Problem

• Convert argumentative texts into structured knowledge graphs
• Identify missing inference rules and undetectable attacks
• Enable reasoning over implicit argument relations

Innovation

• Convert argumentative texts into knowledge graphs
• Use modus ponens to form arguments
• Identify missing inference rules via markers